Progression and regression in chest X-ray with lung cancer incidence and mortality in Two Screening Trials
Principal Investigator
Name
Ya Liu
Degrees
M.D.
Institution
Tianjin Medical University Cancer Institute and Hospital
Position Title
None
Email
About this CDAS Project
Study
PLCO
(Learn more about this study)
Project ID
PLCO-1090
Initial CDAS Request Approval
Nov 1, 2022
Title
Progression and regression in chest X-ray with lung cancer incidence and mortality in Two Screening Trials
Summary
Lung cancer (LC) ranks as the leading cause of cancer incidence and mortality in men for several years, whereas, in women, it is third for incidence and second for mortality in 2020.[1]. In 2020, an estimated 2.2 million new LC cases and 1.8 million LC deaths occurred, which accounted for 11.4% of all new cancer cases, and 18.0% of all cancer deaths[2]. Due to the emerging aging trend, stubbornly high tobacco epidemic, and surging air pollution around the world, the LC incidence is expected to continually rise in many countries in the future. Reducing the increasing burden of LC has become a global concern faced by several countries, especially by transitioning or developing countries.
Screening for LC with chest X-ray (CXR) or sputum cytology has long been studied as an approach to reduce the burden of LC since the 1970s. Although low-dose computed tomography altered the landscape of LC screening since 2011 and screening with CXR failed to detect a significant reduction in LC mortality, chest X-ray (CXR) is still widely used as fundamental exam for LC screening in several resource-limited regions due to lack of either the equipment or the technicians. More importantly, with the widespread rise of artificial intelligence (AI), Deep learning-based automatic diagnostic model based on CXR is highly expected to significantly improve the early detection rate of LC. However, before the sophisticated AI-assisted CXR diagnostic technology is widely used in population-based screening for LC, how to reduce the potential missed diagnosis, false positive and overdiagnosis associated with traditional CXR exam is the key to improve the effect of CXR screening. Ignoring progression after negative CXR and regression after positive CXRs are presumed to be the leading causes of the above limitations in CXR screening. Independent evaluation of CXR without reference to potential risk factors for LC may also contribute to the diluted effectiveness of CXR screening for LC. However, until now, few studies have investigated the associations of progression and regression with LC incidence, and no study has explored the stratified associations by smoking and the potential factors associated with progression and regression.
Therefore, in this study, based on the multi-round CXR screenings from the Prostate, Lung, Colorectal, and Ovarian Cancer Screening (PLCO) trial and the National Lung Screening Trial (NLST), we aimed to investigate the associations of progression and regression with LC incidence, LC mortality, and all-cause mortality, and further to evaluate the stratified associations by smoking and the potential factors associated with progression and regression. Finally, meta-analyses were conducted to achieve the pooled results beyond the individual study.
Screening for LC with chest X-ray (CXR) or sputum cytology has long been studied as an approach to reduce the burden of LC since the 1970s. Although low-dose computed tomography altered the landscape of LC screening since 2011 and screening with CXR failed to detect a significant reduction in LC mortality, chest X-ray (CXR) is still widely used as fundamental exam for LC screening in several resource-limited regions due to lack of either the equipment or the technicians. More importantly, with the widespread rise of artificial intelligence (AI), Deep learning-based automatic diagnostic model based on CXR is highly expected to significantly improve the early detection rate of LC. However, before the sophisticated AI-assisted CXR diagnostic technology is widely used in population-based screening for LC, how to reduce the potential missed diagnosis, false positive and overdiagnosis associated with traditional CXR exam is the key to improve the effect of CXR screening. Ignoring progression after negative CXR and regression after positive CXRs are presumed to be the leading causes of the above limitations in CXR screening. Independent evaluation of CXR without reference to potential risk factors for LC may also contribute to the diluted effectiveness of CXR screening for LC. However, until now, few studies have investigated the associations of progression and regression with LC incidence, and no study has explored the stratified associations by smoking and the potential factors associated with progression and regression.
Therefore, in this study, based on the multi-round CXR screenings from the Prostate, Lung, Colorectal, and Ovarian Cancer Screening (PLCO) trial and the National Lung Screening Trial (NLST), we aimed to investigate the associations of progression and regression with LC incidence, LC mortality, and all-cause mortality, and further to evaluate the stratified associations by smoking and the potential factors associated with progression and regression. Finally, meta-analyses were conducted to achieve the pooled results beyond the individual study.
Aims
Based on the multi-round CXR screenings from the Prostate, Lung, Colorectal, and Ovarian Cancer Screening (PLCO) trial and the National Lung Screening Trial (NLST), we aimed to investigate the associations of progression and regression with LC incidence, LC mortality, and all-cause mortality, and further to evaluate the stratified associations by smoking and the potential factors associated with progression and regression. Meta-analyses were conducted to achieve the pooled results beyond the individual study.
Collaborators
Fengju Song,Tianjin Medical University Cancer Institute and Hospital
Yubei Huang,Tianjin Medical University Cancer Institute and Hospital
Zhangyan Lyu,Tianjin Medical University Cancer Institute and Hospital